Quick Answer
The answer is standard Salesforce objects like Account and Opportunity, along with Data Cloud objects using the harmonized data model. This is correct because Einstein Prediction Builder requires structured data that can be mapped to a prediction objective, and both of these sources provide the clean, relational schema needed for model training. Standard objects offer familiar fields and relationships, while Data Cloud’s harmonized model unifies Salesforce and external data into a standardized format that Prediction Builder can consume directly. On the Salesforce AI Associate exam, this question tests your understanding of which data sources meet the structured data requirements, often appearing as a multiple-select item where you must avoid choosing unstructured sources like files or streams. A common trap is assuming any Salesforce object works, but only those with sufficient historical data and clear prediction targets are viable. Memory tip: think “Standard and Harmonized” — both are structured and ready for prediction.
This AI Associate practice question tests your understanding of data for ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO data sources can be used with Einstein Prediction Builder?
Files uploaded to Salesforce Files.
Why wrong: Files are not a structured data source.
Data Cloud objects using the harmonized data model.
Data Cloud objects are supported.
Standard Salesforce objects like Account and Opportunity.
These are common data sources.
Chatter feed posts.
Why wrong: Chatter is unstructured text.
Dashboard and report snapshots.
Why wrong: Reports are not data sources for predictions.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Data Cloud objects using the harmonized data model.
Einstein Prediction Builder requires structured data that can be mapped to a prediction objective. Data Cloud objects using the harmonized data model provide a unified, standardized schema that Prediction Builder can consume directly, enabling predictions across multiple Salesforce and external data sources. Standard Salesforce objects like Account and Opportunity are also supported because they contain the fields and relationships needed to train predictive models.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Files uploaded to Salesforce Files.
Why it's wrong here
Files are not a structured data source.
Data Cloud objects using the harmonized data model.
Why this is correct
Data Cloud objects are supported.
Related concept
Read the scenario before looking for a memorised answer.
Standard Salesforce objects like Account and Opportunity.
Why this is correct
These are common data sources.
Related concept
Read the scenario before looking for a memorised answer.
Chatter feed posts.
Why it's wrong here
Chatter is unstructured text.
Dashboard and report snapshots.
Why it's wrong here
Reports are not data sources for predictions.
Common exam traps
Salesforce often tests the misconception that any data in Salesforce (like files or Chatter posts) can be used directly with Einstein Prediction Builder, when in fact only structured, field-level data from objects or harmonized Data Cloud objects is supported.
Detailed technical explanation
Einstein Prediction Builder uses a supervised machine learning model that trains on historical data from selected objects. The harmonized data model in Data Cloud normalizes data from multiple sources (e.g., Salesforce, external systems) into a common schema, allowing Prediction Builder to access fields like 'Total_Spend__c' or 'Last_Contact_Date__c' across datasets. Under the hood, the builder automatically selects relevant features and handles missing values, but it cannot process unstructured data like files or feed posts without prior transformation into structured fields.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: Data Cloud objects using the harmonized data model. — Einstein Prediction Builder requires structured data that can be mapped to a prediction objective. Data Cloud objects using the harmonized data model provide a unified, standardized schema that Prediction Builder can consume directly, enabling predictions across multiple Salesforce and external data sources. Standard Salesforce objects like Account and Opportunity are also supported because they contain the fields and relationships needed to train predictive models.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.
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